Perhaps the most common challenge to the systematic and routine comparisons of data from the model output/analysis with NASA remote sensing data is that of co-locating diverse datasets, often obtained from instruments with different observation characteristics, for the purposes of detailed comparison, e.g. Observations for Model Intercomparisons Project (Obs4MIPS) (Teixeira et al 2014), and use, e.g. adaptation to climate change. Indeed, while a myriad of tools exist to locate, access, and visualize observational data, inter-comparison of disparate data sources requires tedious and often computationally intensive gridding or regridding, which is generally implemented in an ad hoc manner by individual users and becomes an obstacle to robust comparisons.

In addition to the duplication of effort stemming from the lack of standard gridding tools, expediency on the part of individual researchers often results in sub-optimal gridded products that (1) limit accuracy due to failure to adequately account for spatial/temporal sampling bias, (2) lack robust gridding uncertainty estimates, and (3) omit the provenance, all of which limit the value of the products to other researchers. Simplicity and familiarity also drive researchers to apply traditional latitude-longitude (lat-lon) grids rather than better alternatives.

Moreover, the trend in the modeling community is to transition to next-generation grid systems, such as geodesic and cubed-sphere, that possess superior quasi-equiareal, scalable characteristics. To maintain numerical stability, the CFL criterion for the time step used in the numerical integration of the general circulation models must decrease with the smallest distance be-tween any two grid points. Since the meridians converge to a single point at each pole, as the model’s spatial resolution increases, the smallest grid distance of a lat-lon grid system quickly approaches zero. Considerable computation will thus be wasted for high-resolution models based on lat-lon grids, for the short time step required near the poles becomes, progressively, a greater overkill for grid cells toward the equator. Approaches to filter the high-frequency signal to sidestep this concern introduce severe constraints on parallel performance.

Anticipating the need for converting and adapting NASA Earth science remote sensing data for compatibility with results from these next-generation models, we are proposing NOGGIn as an open-access service to enable routine and systematic gridding, colocation, and comparison of remote sensing data that not only makes adapting observations to these grids easy but also ad-dresses a number of gridding issues that currently plague researchers. In particular, this service will:

Grid data onto next-generation icosahedral and cubed-sphere grids, as well as traditional lat-lon grids, with flexible temporal binning (e.g., by local time instead of UTC);

Embed provenance meta-data, describing the operations applied, as well as the original data sources, to improve traceability; and

Allow for introduction of additional estimation methods with a modular design.

We will partner with NASA's MODIS Adaptive Processing System (MODAPS) to leverage and augment its existing map projection web services and to eventually extend NOGGIn as a service in the cloud. We will develop a web client that provides a user interface to these services, making them accessible through a web browser.

Our Earth Science Focus Area is “Water and Energy Cycle”. We will design and implement NOGGin to “improve data access, management and interoperability” with the intent to “increase the efficiency for the user and enable new users to benefit Earth” and be compatible with NASA’s “distributed heterogeneous data and information system architecture” and NASA’s Earth Science “system of systems” infrastructure.